Downloads · 30 days
0
robotic-vt-grasp-project/SGA-GSN
SGA-GSN is a robotics model from robotic-vt-grasp-project. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
This staging repository contains inference-only checkpoints for SGA-GSN grasp-stability prediction and representative baselines. It is intended to accompany the public SGA-GSN code repository and the 3DA-VTG dataset r…
Downloads · 30 days
0
Access
Public
Updated Jun 12, 2026
Repo size
1.1 GB
Likes
0
Public
Click a slice to open those files.
.pth1.1 GB · 100%
From the Hugging Face model README
This staging repository contains inference-only checkpoints for SGA-GSN grasp-stability prediction and representative baselines. It is intended to accompany the public SGA-GSN code repository and the 3DA-VTG dataset release.
License: MIT. This follows the upstream AdaPoinTr/PoinTr license observed in
/AdaPoinTr/LICENSE (Copyright (c) 2021 Xumin Yu). The staged model weights
are released under the same MIT terms as the public SGA-GSN code. Dataset and
simulation assets are separate releases with their own license terms.
| Release file | Role | Epoch | Main metrics |
|---|---|---|---|
checkpoints/shape_completion/ap_ps55.pth | AdaPoinTr shape completion checkpoint required by 3D SGA-GSN and RL perception | 512 | F-Score 0.7315248407, CDL1 8.8670780700 |
checkpoints/sga_gsn/ppct_bce_vtencdrop_8dep_vds_featex_best.pth | Main PPCT/SGA-GSN grasp-stability checkpoint used by the RL stack | 22 | AvgAcc 0.7915571520, F1 0.8032546122 |
checkpoints/baselines/dgcnn_14m_best.pth | Smaller 3D DGCNN baseline | 28 | AvgAcc 0.7879940271, F1 0.8019056932 |
checkpoints/baselines/dgcnn_45m_best.pth | Larger 3D DGCNN baseline | 29 | AvgAcc 0.7860487511, F1 0.7978581551 |
checkpoints/baselines/cnn_best.pth | 2D CNN baseline | 10 | AvgAcc 0.7031855615, F1 0.6832639230 |
checkpoints/baselines/cnnmca_best.pth | 2D CNNMCA / MCACNN baseline | 22 | AvgAcc 0.7680102085, F1 0.7633521055 |
All checkpoints are inference_only files. They keep base_model, epoch, metrics, and best_metrics; optimizer states are intentionally omitted.
The configs/ directory contains release snapshots for the staged checkpoints:
configs/VTG_PPCT.yaml
configs/VTG_DGCNN_14M.yaml
configs/VTG_DGCNN_45M.yaml
configs/VTG_CNN.yaml
configs/VTG_CNNMCA.yaml
configs/dataset_configs/
VTG_PPCT.yaml is aligned to the source experiment config used by the released PPCT checkpoint, because the shorter public training config does not load this checkpoint exactly. The other release configs are copied from the public SGA-GSN code tree. For the 2D CNN and CNNMCA release configs, pretrained is set to False to avoid torchvision downloads before loading the released checkpoint. Loading the checkpoint restores the trained backbone weights.
This model repo does not include code, datasets, mesh assets, or simulation assets. A working setup requires:
https://github.com/Yiju1213/SGA-GSN.gitThe RL stack currently expects the PPCT checkpoint and shape checkpoint together:
shape_checkpoint: checkpoints/shape_completion/ap_ps55.pth
grasp_checkpoint: checkpoints/sga_gsn/ppct_bce_vtencdrop_8dep_vds_featex_best.pth
From the repository root:
sha256sum -c checksums.sha256
The staged checkpoints were also loaded through the public /SGA-GSN model builders in the original vt-grasp-0 container.
metadata/checkpoint_manifest.json records release paths, source paths, file sizes, SHA256 values, source metrics, and omitted keys.metadata/metrics.json provides a compact metrics table for the staged checkpoints.metadata/training_provenance.yaml and metadata/dependencies.yaml document source code, data, and runtime dependencies.